Triple
T18405430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | International Relations Program |
E450110
|
entity |
| Predicate | contextualFocus |
P86548
|
FINISHED |
| Object | global politics in the Asia-Pacific region |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: global politics in the Asia-Pacific region | Statement: [International Relations Program, contextualFocus, global politics in the Asia-Pacific region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: contextualFocus Context triple: [International Relations Program, contextualFocus, global politics in the Asia-Pacific region]
-
A.
importFocus
Indicates that attention, priority, or emphasis is being brought into or concentrated on a particular entity or aspect.
-
B.
canonicalFocus
Indicates that one entity is the primary or most representative focus or point of attention in relation to another entity.
-
C.
focusOf
chosen
Indicates that one entity is the primary subject, target, or center of attention, activity, or interest for another entity.
-
D.
focusShift
Indicates a change in attention or emphasis from one entity or topic to another.
-
E.
focusType
Indicates the specific kind or category of focus or attention that is being applied to or associated with an entity or interaction.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8b9fab8a8819086a9ddc0871715e0 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e51956b8c88190b863e66871825014 |
completed | April 19, 2026, 6:05 p.m. |
| PD | Predicate disambiguation | batch_69e469bf7f74819096a01173493412c2 |
completed | April 19, 2026, 5:35 a.m. |
Created at: April 10, 2026, 10:46 a.m.